Constructed from standardized language tests designed for native speakers, VGaokao requires models to understand more complex passages, compared to other datasets from examinations for second language learners such as C\(^3\)  [1]}.
The main differences can be summarized in three folds.
Firstly, in VGaokao, the vocabulary size is 1.6 times larger than that of C\(^3\) , which brings more diverse words.
Secondly, at the sentence level, the average sentence length of VGaokao is 1.6 times longer than that of C\(^3\) , and the average dependency tree depth of VGaokao is 1.2 times larger than that of C\(^3\) .
Longer sentences and more complicated syntactic structures usually exhibit rich linguistic phenomena, thus requiring models to learn more sophisticated language understanding skills.
Lastly, the passages in VGaokao, which contain 1,159 Chinese characters on average, are approximately 3 times longer than that of RACE [2]} and 10 times longer than that of C\(^3\)  [1]}.
The length of most passages even exceeds the maximum input length of general pre-trained language models such as BERT[4]}.
To exploit the long passages, models may need to take discourse structures into consideration so as to better integrate multiple evidence sentences.
